Home > Research > Publications & Outputs > Forecasting Realised Volatility Using ARFIMA an...

Associated organisational units

Electronic data

  • Volatility Forecasting Paper_QF

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 24/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/14697688.2019.1600713

    Accepted author manuscript, 1003 KB, PDF document

    Embargo ends: 24/10/20

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Forecasting Realised Volatility Using ARFIMA and HAR Models

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>1/10/2019
<mark>Journal</mark>Quantitative Finance
Issue number10
Volume19
Number of pages12
Pages (from-to)1627-1638
Publication statusPublished
Early online date24/04/19
Original languageEnglish

Abstract

Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.